Germany Petrol Stations Locations Dataset – Top 16 Petrol Station Brands
Germany Petrol Stations Locations Dataset – Top 16 Petrol Station Brands
Top 16 Petrol Station Chains Across Germany: Premium Multi-Brand Location Dataset
Unlock a complete, ready-to-use dataset featuring the leading Petrol Station brands in Germany. This expertly compiled Top Brands dataset consolidates all major Petrol Station chains in Germany, delivering a clean, standardised and analysis-ready collection of 10,976 locations. Perfect for businesses seeking high-quality data for market sizing, competitive landscape analysis, network optimisation, and strategic planning.
Last updated: 7 July 2026.
Dataset fields included in the CSV:
- GUID
- Title
- Group
- Latitude
- Longitude
- Street No
- Street
- Area
- City
- Admin_level_1
- Admin_level_2
- Gemainde
- Federal State
- Population
- Postal Code
- Address
- Wheelchair
- Popularity Score
- Phone
- Website
- Fuel Type
- Opening hours
Brand distribution
- Aral – 2,421 locations in Germany
- AS 24 – 167 locations in Germany
- AVIA – 897 locations in Germany
- Classic – 185 locations in Germany
- Eni (Agip) – 313 locations in Germany
- Esso – 1,270 locations in Germany
- HEM – 442 locations in Germany
- Hoyer – 352 locations in Germany
- JET – 749 locations in Germany
- OIL! – 271 locations in Germany
- Raiffeisen – 639 locations in Germany
- Shell – 1,719 locations in Germany
- Star (Orlen) – 519 locations in Germany
- tankpool24 – 640 locations in Germany
- Westfalen – 392 locations in Germany
Petrol Stations distribution by Federal State
- Nordrhein-Westfalen: 2,256 petrol stations
- Bayern: 1,856 petrol stations
- Niedersachsen: 1,503 petrol stations
- Baden-Württemberg: 1,406 petrol stations
- Hessen: 858 petrol stations
- Rheinland-Pfalz: 548 petrol stations
- Schleswig-Holstein: 488 petrol stations
- Sachsen: 379 petrol stations
- Brandenburg: 319 petrol stations
- Sachsen-Anhalt: 317 petrol stations
- Thüringen: 283 petrol stations
- Mecklenburg-Vorpommern: 281 petrol stations
- Hamburg: 152 petrol stations
- Berlin: 137 petrol stations
- Saarland: 109 petrol stations
- Bremen: 83 petrol stations
Direct access after purchase of the complete locations dataset for Germany
Download the dataset immediately in a clean, analysis-ready CSV format. Ideal for geospatial analysis, competitor benchmarking, market studies, site planning, and retail location intelligence workflows.
Evaluate the dataset before purchase
A free sample is available for download, allowing you to inspect the structure, fields, and geospatial precision before purchasing the full dataset.
Related geospatial datasets
- Administrative boundaries and full polygon dataset for Germany: Map these petrol stations locations against highly precise administrative polygons for territory analysis and spatial structuring.
- Demographics dataset for Germany: Overlay demographic indicators to deeply understand the population structures and household types surrounding these petrol stations locations.
- Explore demographics data insights for Germany: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Income indicators dataset for Germany (small-area income analysis): Download an analysis-ready dataset on income distribution across small geographic units - ideal for segmentation, customer analytics, location planning, and socio-economic scoring.
- Explore a rich library of Germany-specific datasets on our dedicated country page: detailed demographics, wealth indicators, multi-level boundaries, and a broad spectrum of retail POIs. View demographics, retail POI, and administrative boundary datasets for Germany
Need the data in another format?
We can deliver this dataset in alternative formats upon request (GeoJSON, Shapefile, Excel, PostgreSQL import files, etc.). Contact us at contact@geolocet.com.
Frequently Asked Questions
Q: Can this dataset be used for logistics planning?
A: Yes. Many customers use these datasets to optimize delivery territories, distribution networks, route planning, and last-mile logistics analysis.
Q: Can I integrate this dataset into a PostgreSQL/PostGIS database?
A: Yes. The dataset structure is compatible with PostgreSQL/PostGIS and other relational spatial databases.
Q: Can I use this dataset for proximity analysis?
A: Yes. The geocoded coordinates are suitable for drive-time analysis, catchment modeling, nearest-neighbor analysis, and accessibility studies.
Q: Can this dataset be imported into Power BI or Tableau?
A: Yes. The CSV structure is compatible with Power BI, Tableau, Looker Studio, and other business intelligence platforms.
Q: Can this dataset support territory optimization?
A: Yes. The dataset is suitable for defining service territories, balancing regional coverage, and optimizing operational footprints.
Q: Does the dataset include opening hours?
A: Yes, opening hours are included where publicly available and validated during the data standardization process.
Q: Does the dataset include latitude and longitude coordinates?
A: Yes. Each location record includes precise WGS84 latitude and longitude coordinates for geospatial analysis and mapping workflows.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this this multi-brand dataset to identify underserved regions in Germany for potential market expansion.""Identify isolated this multi-brand sites in Germany that may face operational inefficiencies due to low regional clustering.""Identify fast-growing suburban areas in Germany that currently have limited access to nearby petrol stations."